Meta-QTL (MQTL) analysis is a robust approach for genetic dissection of complex quantitative traits. Rice varieties adapted to non-flooded cultivation are highly desirable in breeding programs due to the water deficit global problem. In order to identify stable QTLs for major agronomic traits under water deficit conditions, we performed a comprehensive MQTL analysis on 563 QTLs from 67 rice populations published from 2001 to 2019. Yield and yield-related traits including grain weight, heading date, plant height, tiller number as well as root architecture-related traits including root dry weight, root length, root number, root thickness, the ratio of deep rooting and plant water content under water deficit condition were investigated. A total of 61 stable MQTLs over different genetic backgrounds and environments were identified. The average confidence interval of MQTLs was considerably refined compared to the initial QTLs, resulted in the identification of some well-known functionally characterized genes and several putative novel CGs for investigated traits. Ortho-MQTL mining based on genomic collinearity between rice and maize allowed identification of five ortho-MQTLs between these two cereals. The results can help breeders to improve yield under water deficit conditions.

Two of the important traits for wheat yield are tiller and fertile tiller number, both of which have been thought to increase cereal yield in favorable and unfavorable environments. A total of 6,349 single nucleotide polymorphism (SNP) markers from the 15 K wheat Infinium array were employed for genome-wide association study (GWAS) of tillering number traits, generating a physical distance of 14,041.6 Mb based on the IWGSC wheat genome sequence. GWAS analysis using Fixed and random model Circulating Probability Unification (FarmCPU) identified a total of 47 significant marker-trait associations (MTAs) for total tiller number (TTN) and fertile tiller number (FTN) in Iranian bread wheat under different water regimes. After applying a 5% false discovery rate (FDR) threshold, a total of 13 and 11 MTAs distributed on 10 chromosomes were found to be significantly associated with TTN and FTN, respectively. Linked single nucleotide polymorphisms for IWB39005 (2A) and IWB44377 (7A) were highly significantly associated (FDR < 0.01) with TTN and FTN traits. Moreover, to validate GWAS results, meta-analysis was performed and 30 meta-QTL regions were identified on 11 chromosomes. The integration of GWAS and meta-QTLs revealed that tillering trait in wheat is a complex trait which is conditioned by the combined effects of minor changes in multiple genes. The information provided by this study can enrich the currently available candidate genes and genetic resources pools, offering evidence for subsequent analysis of genetic adaptation of wheat to different climatic conditions of Iran and other countries.

Thymol, as a dietary monoterpene, is a phenol derivative of cymene, which is the major component of the essential oil of Trachyspermum ammi (L.). It shows multiple biological activities: antifungal, antibacterial, antivirus and anti-inflammatory. T. ammi, commonly known as ajowan, belongs to Apiaceae and is an important medicinal seed spice. To identify the putative genes involved in thymol and other monoterpene biosynthesis, we provided transcriptomes of four inflorescence tissues of two ajowan ecotypes, containing different thymol yield. This study has detected the genes encoding enzymes for the go-between stages of the terpenoid biosynthesis pathways. A large number of unigenes, differentially expressed between four inflorescence tissues of two ajowan ecotypes, was revealed by a transcriptome analysis. Furthermore, differentially expressed unigenes encoding dehydrogenases, transcription factors, and cytochrome P450s, which might be associated with terpenoid diversity in T. ammi, were identified. The sequencing data obtained in this study formed a valuable repository of genetic information for an understanding of the formation of the main constituents of ajowan essential oil and functional analysis of thymol-specific genes. Comparative transcriptome analysis led to the development of new resources for a functional breeding of ajowan.

Background: Olive oil contains monounsaturated oleic acid up to 83% and phenolic compounds, making it an excellent source of fat. Due to its economic importance, the quantity and quality of olive oil should be improved in parallel with international standards. In this study, we analyzed the raw RNA-seq data with a meta-analysis approach to identify important genes and their metabolic pathways involved in olive oil quality. Results: A deep search of RNA-seq published data shed light on thirty-nine experiments associated with the olive transcriptome, four of these proved to be ideal for meta-analysis. Meta-analysis confirmed the genes identified in previous studies and released new genes, which were not identified before. According to the IDR index, the meta-analysis had good power to identify new differentially expressed genes. The key genes were investigated in the metabolic pathways and were grouped into four classes based on the biosynthetic cycle of fatty acids and factors that affect oil quality. Galactose metabolism, glycolysis pathway, pyruvate metabolism, fatty acid biosynthesis, glycerolipid metabolism, and terpenoid backbone biosynthesis were the main pathways in olive oil quality. In galactose metabolism, raffinose is a suitable source of carbon along with other available sources for carbon in fruit development. The results showed that the biosynthesis of acetyl-CoA in glycolysis and pyruvate metabolism is a stable pathway to begin the biosynthesis of fatty acids. Key genes in oleic acid production as an indicator of oil quality and critical genes that played an important role in production of triacylglycerols were identified in different developmental stages. In the minor compound, the terpenoid backbone biosynthesis was investigated and important enzymes were identified as an interconnected network that produces important precursors for the synthesis of a monoterpene, diterpene, triterpene, tetraterpene, and sesquiterpene biosynthesis. Conclusions: The results of the current investigation can produce functional data related to the quality of olive oil and would be a useful step in reducing the time of cultivar screening by developing gene specific markers in olive breeding programs, releasing also new genes that could be applied in the genome editing approach.

A high-quality transcriptome is required to advance numerous bioinformatics workflows. Nevertheless, the effectuality of tools for de novo assembly and real precision assembled transcriptomes looks somewhat unexplored, particularly for non-model organisms with complicated (very long, heterozygous, polyploid) genomes. To disclose the performance of various transcriptome assembly programs, this study built 11 single assemblies and analyzed their performance on some significant reference-free and reference-based criteria. As well as to reconfirm the outputs of benchmarks, 55 BLAST were performed and compared using 11 constructed transcriptomes. Concisely, normalized benchmarking demonstrated that Velvet–Oases suffer from the worst results, while the EvidentialGene strategy can provide the most comprehensive and accurate transcriptome of Lilium ledebourii (Baker) Boiss. The BLAST results also confirmed the superiority of EvidentialGene, so it could capture even up to 59% more (than Velvet–Oases) unique gene hits. To promote assembly optimization, with the help of normalized benchmarking, PCA and AHC, it is emphasized that each metric can only provide part of the transcriptome status, and one should never settle for just a few evaluation criteria. This study supplies a framework for benchmarking and optimizing the efficiency of assembly approaches to analyze RNA-Seq data and reveals that selecting an inefficient assembly strategy might result in less identification of unique gene hits.

Introduction: The balanced fatty acid profile of olive oil not only enhances its stability but also contributes to its positive effects on health, making it a valuable dietary choice. Olive oil's high content of unsaturated fatty acids and low content of saturated fatty acids contribute to its beneficial effects on cardiovascular diseases and cancer. The quantities of these fatty acids in olive oil may fluctuate due to various factors, with genotype being a crucial determinant of the oil's quality.Methods: This study investigated the genetic basis of oil quality by comparing the transcriptome of two Iranian cultivars with contrasting oil profiles: Mari, known for its high oleic acid content, and Shengeh, characterized by high linoleic acid at Jaén index four.Results and discussion: Gas chromatography confirmed a significant difference in fatty acid composition between the two cultivars. Mari exhibited significantly higher oleic acid content (78.48%) compared to Shengeh (48.05%), while linoleic acid content was significantly lower in Mari (4.76%) than in Shengeh (26.69%). Using RNA sequencing at Jaén index four, we analyzed genes involved in fatty acid biosynthesis. Differential expression analysis identified 2775 genes showing statistically significant differences between the cultivars. Investigating these genes across nine fundamental pathways involved in oil quality led to the identification of 25 effective genes. Further analysis revealed 78 transcription factors and 95 transcription binding sites involved in oil quality, with BPC6 and RGA emerging as unique factors. This research provides a comprehensive understanding of the genetic and molecular mechanisms underlying oil quality in olive cultivars. The findings have practical implications for olive breeders and producers, potentially streamlining cultivar selection processes and contributing to the production of high-quality olive oil.